{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize']=(21,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "eta = np.array([3,0,1])\n",
    "T   = np.array([100,1,0.1,0.01])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(eta):\n",
    "    \"\"\"计算softmax值\n",
    "    eta: shape=(d,)\n",
    "    \"\"\"\n",
    "    mu = np.exp(eta)/np.sum(np.exp(eta))\n",
    "    return mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=plt.subplot(141);ax2=plt.subplot(142);ax3=plt.subplot(143);ax4=plt.subplot(144)\n",
    "ax = [ax1,ax2,ax3,ax4]\n",
    "#label = ['(a)','(b)','(c)']\n",
    "axis_title = ['T=100','T=1','T=0.1','T=0.01']\n",
    "for i in range(len(T)):\n",
    "    ax[i].vlines(np.arange(len(eta)),[0]*len(eta),softmax(eta/T[i]),linewidth=5,color='blue')\n",
    "    #ax[i].set_xlabel(label[i])\n",
    "    ax[i].set_title(axis_title[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
